41–50 of 279 posts
I was under the impression that CoT works because spitting out more tokens = more context = more compute used to "think." Using CoT as a way for LLMs "show their working" never seemed logical, to me. It's just extra synthetic context.
The fact that it was ever seriously entertained that a "chain of thought" was giving some kind of insight into the internal processes of an LLM bespeaks the lack of rigor in this field. The words that are coming out of the model are generated to optimize for RLHF and closeness to the training data, that's it! They aren't references to internal concepts, the model is not aware that it's doing anything so how could it…
You should read OpenAI's brief on the issue of fair use in its cases. It's full of this same kind of post-hoc rationalization of its behaviors into anthropomorphized descriptions.
OpenAI made a big show out of hiding their reasoning traces and using them for alignment purposes [0]. Anthropic has demonstrated (via their mech interp research) that this isn't a reliable approach for alignment.
The fact that it was ever seriously entertained that a "chain of thought" was giving some kind of insight into the internal processes of an LLM bespeaks the lack of rigor in this field. The words that are coming out of the model are generated to optimize for RLHF and closeness to the training data, that's it! They aren't references to internal concepts, the model is not aware that it's doing anything so how could it…
Yep. They aren't stupid. They aren't smart. They don't do smart. They don't do stupid. They do not think . They don't even " they ", if you will. The forms of their input and output are confusing people into thinking these are something they're not, and it's really frustrating to watch. [EDIT] The forms of their input & output and deliberate hype from "these are so scary! ... Now pay us for one" Altman and others, I…
The fact that it was ever seriously entertained that a "chain of thought" was giving some kind of insight into the internal processes of an LLM bespeaks the lack of rigor in this field. The words that are coming out of the model are generated to optimize for RLHF and closeness to the training data, that's it! They aren't references to internal concepts, the model is not aware that it's doing anything so how could it…
>internal concepts, the model is not aware that it's doing anything so how could it "explain itself" This in a nutshell is why I hate that all this stuff is being labeled as AI. Its advanced machine learning (another term that also feels inaccurate but I concede is at least closer to whats happening conceptually) Really, LLMs and the like still lack any model of intelligence. Its, in the most basic of terms, algorith…
Regardless of how it models intelligence, why is it not AI? Do you mean it is not AGI? A system that can take a piece of text as input and output a reasonable response is obviously exhibiting some form of intelligence, regardless of the internal workings.
Earlier quoted context omitted.
> When we get to the point where a LLM can say "oh, I made that mistake because I saw this in my training data, which caused these specific weights to be suboptimal, let me update it", that'll be AGI. While I believe we are far from AGI, I don't think the standard for AGI is an AI doing things a human absolutely cannot do.
We're far from AI. There is no intelligence. The fact the industry decided to move the goal post and re-brand AI for marketing purposes doesn't mean they had a right to hijack a term that has decades of understood meaning. They're using it to bolster the hype around the work, not because there has been a genuine breakthrough in machine intelligence, because there hasn't been one. Now this technology is incredibly use…
The fact that it was ever seriously entertained that a "chain of thought" was giving some kind of insight into the internal processes of an LLM bespeaks the lack of rigor in this field. The words that are coming out of the model are generated to optimize for RLHF and closeness to the training data, that's it! They aren't references to internal concepts, the model is not aware that it's doing anything so how could it…
>internal concepts, the model is not aware that it's doing anything so how could it "explain itself" This in a nutshell is why I hate that all this stuff is being labeled as AI. Its advanced machine learning (another term that also feels inaccurate but I concede is at least closer to whats happening conceptually) Really, LLMs and the like still lack any model of intelligence. Its, in the most basic of terms, algorith…
* fixed a typo, used to be "defend"
Earlier quoted context omitted.
>internal concepts, the model is not aware that it's doing anything so how could it "explain itself" This in a nutshell is why I hate that all this stuff is being labeled as AI. Its advanced machine learning (another term that also feels inaccurate but I concede is at least closer to whats happening conceptually) Really, LLMs and the like still lack any model of intelligence. Its, in the most basic of terms, algorith…
While I agree that LLMs are hardly sapient, it's very hard to make this argument without being able to pinpoint what a model of intelligence actually is. "Human brains lack any model of intelligence. It's just neurons firing in complicated patterns in response to inputs based on what statistically leads to reproductive success"
There exists a generally accepted baseline definition for what crosses the threshold of intelligent behavior. We shouldn't seek to muddy this.
EDIT: Generally its accepted that a core trait of intelligence is an agent’s ability to achieve goals in a wide range of environments. This means you must be able to generalize, which in turn allows intelligent beings to react to new environments and contexts without previous experience or input.
Nothing I'm aware of on the market can do this. LLMs are great at statistically inferring things, but they can't generalize which means they lack reasoning. They also lack the ability to seek new information without prompting.
The fact that all LLMs boil down to (relatively) simple mathematics should be enough to prove the point as well. It lacks spontaneous reasoning, which is why the ability to generalize is key
The fact that it was ever seriously entertained that a "chain of thought" was giving some kind of insight into the internal processes of an LLM bespeaks the lack of rigor in this field. The words that are coming out of the model are generated to optimize for RLHF and closeness to the training data, that's it! They aren't references to internal concepts, the model is not aware that it's doing anything so how could it…
This is false, reasoning models are rewarded/punished based on performance at verifiable tasks, not human feedback or next-token prediction.
Earlier quoted context omitted.
>internal concepts, the model is not aware that it's doing anything so how could it "explain itself" This in a nutshell is why I hate that all this stuff is being labeled as AI. Its advanced machine learning (another term that also feels inaccurate but I concede is at least closer to whats happening conceptually) Really, LLMs and the like still lack any model of intelligence. Its, in the most basic of terms, algorith…
One of the earliest things that defined what AI meant were algorithms like A*, and then rules engines like CLIPS. I would say LLMs are much closer to anything that we'd actually call intelligence, despite their limitations, than some of the things that defined* the term for decades. * fixed a typo, used to be "defend"
There have been many attempts to pervert the term AI, which is a disservice to the technologies and the term itself.
Its the simple fact that the business people are relying on what AI invokes in the public mindshare to boost their status and visibility. Thats what bothers me about its misuse so much